A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program
Gastroenterology and Hepatology from Bed to Bench,
Vol. 16 No. 4 (2023),
20 September 2023
https://doi.org/10.22037/ghfbb.v16i4.2843
Abstract
Aim: This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology.
Background: Underestimation is a widespread issue in cancer registries within developing countries.
Methods: This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value.
Results: The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.
The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.
Conclusion: The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.
- Cancer, Registry, Bayesian method, Underestimation, Iran
How to Cite
References
Mao JJ, Pillai GG, Andrade CJ, Ligibel JA, Basu P, Cohen L, et al. Integrative oncology: addressing the global challenges of cancer prevention and treatment. CA Cancer J Clin 2022;72:144-64.
Redondo-Sánchez D, Petrova D, Rodríguez-Barranco M, Fernández-Navarro P, Jiménez-Moleón JJ, Sánchez M-J. Socio-economic inequalities in lung cancer outcomes: an overview of systematic reviews. Cancers 2022;14:398.
Wanner M, Matthes KL, Korol D, Dehler S, Rohrmann S. Indicators of data quality at the cancer registry Zurich and Zug in Switzerland. Biomed Res Int 2018;2018:7656197.
Frech S, Muha CA, Stevens LM, Trimble EL, Brew R, Perin DP, et al. Perspectives on strengthening cancer research and control in Latin America through partnerships and diplomacy: experience of the National Cancer Institute’s Center for Global Health. J Glob Oncol 2018;4:1-11.
Bray F, Parkin DM. Evaluation of data quality in the cancer registry: principles and methods. Part I: comparability, validity and timeliness. Eur J Cancer 2009;45:747-55.
Wei W, Zeng H, Zheng R, Zhang S, An L, Chen R, et al. Cancer registration in China and its role in cancer prevention and control. Lancet Oncol 2020;21:342-9.
Conway D, Purkayastha M, Chestnutt I. The changing epidemiology of oral cancer: definitions, trends, and risk factors. Br Dent J 2018;225:867-73.
Chao A, Tsay P, Lin SH, Shau WY, Chao DY. The applications of capture‐recapture models to epidemiological data. Stat Med 2001;20:3123-57.
Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2015;13:8-17.
Kum H-C, Krishnamurthy A, Machanavajjhala A, Reiter MK, Ahalt S. Privacy preserving interactive record linkage (PPIRL). J Am Med Inform Assoc 2014;21:212-20.
Stephen C. Capture-recapture methods in epidemiological studies. Infect Control Hosp Epidemiol 1996;17:262-6.
Bird SM, King R. Multiple systems estimation (or capture-recapture estimation) to inform public policy. Annu Rev Stat Appl 2018;5:95-118.
Dusetzina SB, Tyree S, Meyer A-M, Meyer A, Green L, Carpenter WR. An overview of record linkage methods. Linking Data for Health Services Research: A Framework and Instructional Guide [Internet]. 2014.
Li G, Shi J. Applications of Bayesian methods in wind energy conversion systems. Renew Energ 2012;43:1-8.
Bon JJ, Bretherton A, Buchhorn K, Cramb S, Drovandi C, Hassan C, et al. Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. Philos Trans Royal Soc A 2023;381:20220156.
Spiegelhalter DJ, Myles JP, Jones DR, Abrams KR. An introduction to Bayesian methods in health technology assessment. Br Med J 1999;319:508-12.
Jack Lee J, Chu CT. Bayesian clinical trials in action. Stat Med 2012;31:2955-72.
Berniker M, Kording K. Bayesian approaches to sensory integration for motor control. Wiley Interdiscip Rev Cogn Sci 2011;2:419-28.
Paulino CD, Soares P, Neuhaus J. Binomial regression with misclassification. Biometrics 2003;59:670-5.
Paulino C, Silva G, Alberto Achcar J. Bayesian analysis of correlated misclassified binary data. CMStatistics 2005;49:1120-31.
Liu Y, Johnson WO, Gold EB, Lasley BL. Bayesian analysis of risk factors for anovulation. Stat Med 2004;23:1901-19.
Stamey JD, Young DM, Seaman JW, Jr. A Bayesian approach to adjust for diagnostic misclassification between two mortality causes in Poisson regression. Stat Med 2008;27:2440-52.
Roshandel G, Sadjadi A, Aarabi M, Keshtkar A, Sedaghat S, Nouraie S, et al. Cancer incidence in Golestan Province: report of an ongoing population-based cancer registry in Iran between 2004 and 2008. Arch Iran Med 2012;15:0-.
Raeisi A, Janbabaei G, Malekzadeh R. Iranian Annual of National Cancer Registration Report, Islamic Republic Iran, Ministry of Health and Medical Education, Health and Treatment Deputy. Center for Disease Control and Prevention, Non communicable Disease Unit, Cancer office. 2008;2009:2019.
Etemadi A, Sadjadi A, Semnani S, Nouraie SM, Khademi H, Bahadori M. Cancer registry in Iran: a brief overview. Arch Iran Med 2008;11:577-80.
Ahmad O, Boschi-Pinto C, Lopez A, Murray C, Lozano R, Inoue M. Age standardization of rates: a new WHO standard. Geneva: World Health Organization 2001;9:1-14.
Roshandel G, Semnani S, Fazel A, Honarvar M, Taziki M, Sedaghat S, et al. Building cancer registries in a lower resource setting: the 10-year experience of Golestan, Northern Iran. Cancer Epidemiol 2018;52:128-33.
Forman D, Bray F, Brewster D, Gombe Mbalawa C, Kohler B, Piñeros M. Cancer incidence in five continents, volume X. IARC scientific publication No. 164. Lyon, France: International Agency for Research on Cancer. 2014.
Roshandel G, Semnani S, Fazel A, Bray F, Malekzadeh R. Cancer epidemiology in Golestan, Iran: 10-year results of Golestan population-based cancer registry (2004-2013). Gorgan: Peyk Rehyan 2017.
Roshandel G, Ghanbari-Motlagh A, Partovipour E, Salavati F, Hasanpour-Heidari S, Mohammadi G, et al. Cancer incidence in Iran in 2014: Results of the Iranian National Population-based Cancer Registry. Cancer Epidemiol 2019;61:50-8.
Somi M, Dolatkhah R, Sepahi S, Belalzadeh M, Sharbafi J, Abdollahi L, et al. Cancer incidence in the East Azerbaijan province of Iran in 2015–2016: results of a population-based cancer registry. BMC Public Health 2018;18:1-13.
Ji J, Sundquist K, Sundquist J, Hemminki K. Comparability of cancer identification among Death Registry, Cancer Registry and Hospital Discharge Registry. Int J Cancer 2012;131:2085-93.
Hajizadeh N, Baghestani AR, Pourhoseingholi MA, Ashtari S, Fazeli Z, Vahedi M, et al. Trend of hepatocellular carcinoma incidence after Bayesian correction for misclassified data in Iranian provinces. World J Hepatol 2017;9:704-10.
Hajizadeh N, Pourhoseingholi MA, Baghestani AR, Abadi A, Zali MR. Bayesian adjustment for over-estimation and under-estimation of gastric cancer incidence across Iranian provinces. World J Gastrointest Oncol 2017;9:87.
Pourhoseingholi MA, Faghihzadeh S, Hajizadeh E, Abadi A, Zali MR. Bayesian estimation of colorectal cancer mortality in the presence of misclassification in Iran. Asian Pac J Cancer Prev 2009;10:691-4.
Ades AE, Sculpher M, Sutton A, Abrams K, Cooper N, Welton N, et al. Bayesian methods for evidence synthesis in cost-effectiveness analysis. Pharmacoeconomics 2006;24:1-19.
Carpenter JR, Smuk M. Missing data: a statistical framework for practice. Biom J 2021;63:915-47.
Ashby D, Smith AF. Evidence‐based medicine as Bayesian decision‐making. Stat Med 2000;19:3291-305.
- Abstract Viewed: 252 times
- PDF Downloaded: 177 times